Methods for the analysis of dissociation melt curve data

ABSTRACT

Methods are provided that operate on raw dissociation data and dissociation curves to generate calibrations of the detected data and to further improve analysis of the data. The data can be taken from each support region of a multi-region platform, for example, from each well of a multi-well plate. Each support region can be loaded with portions of the same sample. In some embodiments, a dissociation curve correction can be calibrated for the sample, prior to a run of an experiment using such sample. In some embodiments, a method is provided for generating a melting transition region of dissociation curves that show the melting characteristics of the sample. In some embodiments, dye temperature dependence correction can be performed on the dissociation curve data to further improve analysis. In some embodiments, a feature vector can be derived from the melt data, and the feature vector can be used to further improve genotyping analysis of the dissociation curves.

FIELD

The field of disclosure of relates to methods for analyzing melt curvedata, especially as the analysis relates to data for which the meltingtemperatures of the plurality of samples varies by only a fraction of adegree.

BACKGROUND

DNA amplification methods provide a powerful and widely used tool forgenomic analysis. Polymerase chain reaction (PCR) methods, for example,permit quantitative analysis to determine DNA copy number, sample sourcequantitation, and transcription analysis of gene expression. Meltingcurve analysis is an important tool used to discriminate realamplification products from artifacts, for genotyping, and for mutationscanning. DNA analysis methods allow the detection of single basechanges in specific regions of the genome, such as single nucleotidepolymorphisms (SNPs). SNP analysis and other techniques facilitate theidentification of mutations associated with specific diseases andconditions, such as various cancers, thalassemia, or others.

Statistical assay variations in melt curve data result from system noisein an analysis system, such as the thermal non-uniformity of athermocycler block in a thermal cycler apparatus. For certain genotypingapplications, the melting point shift between samples may be onlyfractions of a degree. In the case of SNP analysis, the SNP mutationsmay shift the melting point temperature by no more than 0.2° C.

Accordingly, there is a need in the art for methods of analyzing smalldifferences in melting curves in the presence of the inherent noise ofthe analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart that depicts various embodiments of methods forthe analysis of dissociation melt curve data.

FIG. 2 is a flow chart that depicts various embodiments of methods forthe analysis of dissociation melt curve data.

FIG. 3A and FIG. 3B depict a generalized schematic of a thermal cyclersystem.

FIG. 4 depicts a series of dissociation melt curves for a set ofcalibration data.

FIG. 5 depicts the series of graphs of FIG. 4 taken over an estimatedtemperature range according to various embodiments of methods for theanalysis of dissociation melt curve data.

FIG. 6A and FIG. 6B illustrate estimating an asymptote according tovarious embodiments of methods for the analysis of dissociation meltcurve data for the low temperature region of graphs, such as those shownin FIG. 5.

FIG. 7 depicts the series of graphs of FIG. 5 redrawn according tovarious embodiments of methods for the analysis of dissociation meltcurve data shown in FIG. 6A and FIG. 6B.

FIG. 8 depicts a series of dissociation melt curves for a set ofexperimental data according to various embodiments of methods for theanalysis of dissociation melt curve data.

FIG. 9 depicts the series of graphs of FIG. 8 which have been correctedfor assay system variance or noise according to various embodiments ofmethods for the analysis of dissociation melt curve data.

FIG. 10 is a set of experimental data that has been analyzed accordingto various embodiments of methods for the analysis of dissociation meltcurve data.

FIG. 11 is a set of experimental data that has been analyzed accordingto various embodiments of methods for the analysis of dissociation meltcurve data.

FIG. 12 is a graphical form of the experimental data of FIG. 11.

DETAILED DESCRIPTION

What is disclosed herein are various embodiments of methods foranalyzing dissociation melt curve data, or as it is used throughoutherein, melt curve data (MCD), where the differences in the meltingpoints between various samples are small. For example, variousembodiments of methods for analyzing dissociation melt curve dataaddress samples sets where the differences in melting points may vary byonly fractions of degrees. According to various embodiments of methodsfor the analysis of dissociation melt curve data, a calibration set ofmelt curve data may be used as a basis for correcting experimental setsof melt curve data, for example, with respect to assay system varianceor noise. According to various embodiments, the melt curve data may beprocessed using curve-fitting techniques. In various embodiments ofmethods for analyzing dissociation melt curve data, different attributesof dissociation melt curve data, such those generated using a differenceplot, may be used as the basis of cluster analysis of experimental meltcurve data.

One known approach for DNA melting curve analysis utilizes fluorescencemonitoring with intercalating double-strand-DNA specific dyes, such asfor example, SYBR Green. The SYBR Green dye attaches to the DNA asdouble-stranded DNA amplification products are formed, and continues tobind to the DNA as long as the DNA remains double-stranded. When meltingtemperatures are reached, the denaturation or melting of thedouble-stranded DNA is indicated and can be observed by a significantreduction in fluorescence, as SYBR Green dissociates from the meltedstrand. The detected dye fluorescence intensity typically decreasesabout 1000-fold during the melting process. Plotting fluorescence as afunction of temperature as the sample heats through the dissociationtemperature produces a DNA melting curve. The shape and position of theDNA melting curve is a function of the DNA sequence, length, and GC/ATcontent.

Further, various approaches for validating the integrity of PCRreactions rely on melting curve analysis to discriminate artifact fromreal amplification product. Melting curve analysis can also be used todifferentiate the various products of multiplexed DNA amplification, andto extend the dynamic range of quantitative PCR. DNA melting curveanalysis is also used as a powerful tool for optimizing PCR thermalcycling conditions, because the point at which DNA fragments or othermaterial melts and separate can be more accurately pinpointed.

In some embodiments, dissociation curve analysis methods calculate anddisplay the first derivative of multi-component dye intensity dataversus temperature, i.e., the differential melting curve. The meltingtemperature, T_(m), at a peak of the differential melting curve can beused to characterize the product of a biochemical reaction. A samplewith multiple amplification products will show multiple peaks in thedifferential melt curve. In some embodiments, melting curve detectioninvolves very precise measurements of temperature and allows for theidentification of a sample using the melting temperature, T_(m). Thedetermination of T_(m) using various embodiments of methods fordifferential dissociation and melting curve detection is disclosed inrelated in U.S. patent application Ser. No. 12/020,369, which isincorporated herein by reference in its entirety.

According to various embodiments as shown in FIG. 3A and FIG. 3B, asample can be loaded into a sample support device. In variousembodiments, as shown in FIG. 3A, a sample support device 10 comprises asubstrate 11 having substantially planar upper and lower surfaces, 13,15, respectively. Various embodiments of a sample support device mayhave a plurality of sample regions 14 on a surface 13. In variousembodiments, the substrate 11 may be a glass or plastic slide with aplurality of sample regions 14, which may be isolated from the ambientby cover 12. Some examples of a sample support device may include, butare not limited by, a multi-well plate, such as a standard microtiter96-well, a 384-well plate, or a microcard, as depicted in sample supportdevice 20 of FIG. 3B, having a plurality of sample regions or wells 24,which may be isolated from ambient by cover 22. The sample regions invarious embodiments of a sample support device may include depressions,indentations, ridges, and combinations thereof patterned in regular orirregular arrays on the surface of the substrate. In FIG. 3A and FIG.3B, a sample support device is shown placed in a thermal cycler system.In various embodiments of a thermal cycler system, there may be a heatblock, 60, and a detection system 51. The detection system 51 may havean illumination source 52 that emits electromagnetic energy 56, and adetector 54, for receiving electromagnetic energy 57 from samples insample support devices 10 and 20 in FIG. 3A and FIG. 3B, respectively.

In various embodiments, replicate aliquots of a sample can be loadedinto the plate to determine the melting temperature, Tm, of the eachwell. Ideally, these temperatures should be identical throughout thewells, given that the samples are replicates, In practice, variations inthe analysis system, for example, non-uniformity of heating elements ofthe analysis system, create variations in the set of replicates.According to various embodiments of methods for the analysis ofdissociation melt curve data, such melt curve data using replicates maybe used as a calibration set of data. In FIG. 1, step 10, such aplurality of melting points comprises a plurality or set of calibrationmelt curve data (CMCD). Similarly, as indicated in step 20 of FIG. 1, ina separate sample plate, unknown samples of interest for analysis may bedispensed into a plurality of support regions of a sample support deviceto determine the melting temperature of the unknown samples. Such aplurality of melting points comprises a plurality or set of experimentalmelt curve data (EMCD).

According to various embodiments of methods for the analysis ofdissociation melt curve data, as depicted in step 30 of FIG. 1, signalprocessing steps may be applied to the raw dissociation melt curve datain advance of subsequent steps, such scaling, curve fitting, and clusteranalysis. Such signal processing steps may include the correction of theEMCD with respect to assay system variance or noise. Sources of assaysystems noise may include, for example, but not limited by, thermalnon-uniformity, excitation source non-uniformity, and detection sourcenoise.

According to various embodiments as indicated in FIG. 1 step 40, meltcurve data may be processed to remove information that is not relevantfor defining true differences among dissociation melt curves havingmelting temperatures that are different by only fractions of a degree,by scaling the data over an estimated temperature range.

For example, in FIG. 4, by using a set of CMCD, various embodiments ofstep 40 of FIG. 1 may be illustrated. The CMCD shown in FIG. 4represents 96 replicates of a sample, where intensity of the signal isplotted as a function of temperature. Between 50° C. and 55° C., in thelow temperature region of the curve, there are deviations from linearitythat are artifacts, which are irrelevant to the melt curve data.Further, by inspecting FIG. 4, it is apparent that the melting occurs ina region of between about 70° C. to about 90° C. and that intensityapproaches zero at temperatures above the melt. Additionally, the regionfrom about 55° C. to about 80° C. a monotonic decrease in intensity isapparent. This is due to a decrease in the light emitted from thereplicates as a result of the temperature dependence of dye emission,which is known to be an inverse relationship (i.e. dye emissiondecreases as temperature increases).

According to various embodiments of methods for the analysis ofdissociation melt curve data as depicted in step 40 of FIG. 1,curve-fitting of the calibration data may be done based on theobservations that the region between about 50° C. to about 55° C.contains artifacts, the region between 55° C. to about 80° C. should belinear, the melt occurs between about 70° C. to about 90° C., and thehigh temperature region above the melt approaches zero. In variousembodiments, the curve-fitting of the calibration data may additionallyuse the information from a reference well in the calibration set. Forexample, a reference well may be selected as the initially brightestwell in a calibration set before the melt curve analysis is run. A firstderivative may be taken on the reference well melt curve data after theanalysis is complete. The width of the first derivative peak of areference well may be used in conjunction with the observation that themelting occurs in a region of between about 70° C. to about 90° C. todefine the abscissa. Additionally, given that it is known that theregion between 55° C. to about 80° C. should be linear, the ordinate maybe scaled using a relative scale, wherein a maximum value of theordinate scale is set by an intercept of the low temperature end of themelt curve data with the ordinate, and should approach zero at the hightemperature range of the melt curve profile.

According to various embodiments of step 40 of FIG. 1, for the purposeof illustration, the CMCD of FIG. 4 has been scaled to produce the meltcurve data shown in FIG. 5. For FIG. 5, the calibration data of FIG. 4have been fit to an abscissa scaled to between about 70° C. to about 88°C. Additionally, the linear portion of the low melt end of the CMCD havebeen fit to 100 at intercept at the low temperature end of the scale,and approach zero at the high temperature range of the melt curveprofile.

In various embodiments of methods for the analysis of dissociation meltcurve data, in addition to the curve-fitting of step 40 of FIG. 1,additional curve-fitting steps maybe applied to the calibration data.For example, as indicated in step 50 of FIG. 1, according to variousembodiments, it may be desirable to estimate an asymptote at the lowtemperature end of the curve for the purpose of detecting differences indata sets of melt curve data that have melting temperatures that vary byonly fractions of a degree. Various embodiments for estimating anasymptote for the low temperature end of the melt curve data aredepicted in FIGS. 6A and 6B.

In FIG. 6A, line B may be extrapolated from a melt curve A by selectinga linear portion over a narrow region of the low temperature melt range.The linear portion may be selected, according to various embodiments, byan interval of a temperature change at a defined temperature point.According to various embodiments, the defined temperature point may beselected using the first derivative data, and defining a transitionregion, as for example, but not limited by, the full width at half themaximum of the first derivative peak. As one of ordinary skill in theart is apprised, such a transition region corresponds to an interval oftwo standard deviations about the midpoint of the first derivativecurve. As such, other intervals about the curve may also be selected. Invarious embodiments, a temperature point may be selected at the lowtemperature end of the defined transition region, as the low temperatureregion is known to be linear. According to various embodiments, after atemperature point is selected, an interval from the point containingenough data points to extrapolate a line is selected. In that regard,the interval would correspond to at least two data points. According tovarious embodiments, the interval may be at least about 0.1° C. Invarious embodiments, the interval may be at least about 0.5° C. In stillother embodiments, the interval may be at least about 1° C.

For example a temperature point of about 70.0° C. may be selected, withan interval of plus or minus 0.5° C. around the temperature point. Fromthis narrow linear region, a line, such as line B in FIG. 6A can beextrapolated. An algorithm, such as the subtraction of melt curve A andline B, can be used to evaluate a point where the two functions deviateby preset limit. For example, but not limited by, when the differencebetween the two curves is at least as great as, for example, twice theassay noise, then the calculated difference may indicate a significantdifference. Alternatively, in various embodiments, other methods fordetermining a point where the two functions deviate by preset limit,such as the method for detecting nonlinearity in analog circuitanalysis, may be used. Such a preset limit is designated as point C inFIG. 6A. Point C defines a point through which line D is drawnhorizontally through the ordinate, thereby defining an estimatedasymptote for the low temperature region. The calibration melt curve Ais then fit accordingly to this asymptote, line D, as shown in FIG. 6B.In FIG. 7, the calibration data of FIG. 5 have been fit to an estimatedlow temperature asymptote.

Step 40 and step 50 in FIG. 1 can be applied to a set of experimentaldata generated using test samples. The data presented in FIG. 8represent a set of experimental data that have been fit according tovarious embodiments of methods described by step 40 and step 50 ofFIG. 1. The EMCD in FIG. 8 have been clustered according genotype. Ininspecting the EMCD of FIG. 8, there appears to be significant overlapof the melt curve data for the genotypes.

As previously mentioned, as depicted in step 30 of FIG. 1, according tovarious embodiments, signal processing steps may be applied to the rawdissociation melt curve data in advance of steps, such as steps 40 and60 of FIG. 1. Such signal processing steps may include the correction ofthe EMCD with respect to assay system variance or noise, such as, butnot limited by, assay system thermal non-uniformities inherent inthermal cycler systems.

As previously stated, the calibration melt curve data set is generatedfrom replicates of the same sample dispensed in support regions of asample support device, the variations in the calibration data are due tothe inherent assay system noise. Accordingly, the information in thecalibration melt curve data can be used to correct the experimental meltcurve data for system noise. For example, a reference sample region inthe EMCD may be selected. According to various embodiments, thefrequency plot of the intensities of the sample regions, such as a well,in a sample support device may be determined, and a sample region withintwo standard deviations of the peak intensity of the EMCD may beselected as a reference sample region. In various embodiments, thereference sample region of the EMCD corresponding to the greatestintensity may be selected, however any sample region within two standarddeviations would not be an outlier; i.e. either too dim or to bright,for the purpose of selecting a reference sample region, such as a well.According to various embodiments for correcting system noise asindicated in step 20 of FIG. 1, the corresponding sample region for theCMCD is then selected as a CMCD reference sample region, such as a well.In various embodiments, a difference from the CMDC reference sampleregion to any sample region on the sample support may be calculated forany point along the melt curve data, or any form of the melt curve data,such as, but not limited by, derivative data. This correction of thevariation of the sample support regions over the sample support devicedue to assay system noise may then be applied to the EMCD. Other typesof approaches may be used to determine a correction factor. For example,an average of the intensities of the CMCD may be taken over the entireCMCD sample set. For any specific sample region of the CMCD, acorrection may be determined by subtracting the sample region intensityfrom the average. That correction may then be applied to thecorresponding sample region of the EMCD.

A correction as described above for step 20 of FIG. 1 was applied toFIG. 8, and the result is demonstrated in FIG. 9. It is apparent thatthe correction of the experimental data as displayed in FIG. 9 resultsin the ready clustering of the genotypes. A set of EMCD shown in tabularform is presented in FIG. 10. In FIG. 10, the first column displays thepreviously verified genotype of the samples. The second columnrepresents the call made based on experimental data that was notcorrected for assay system noise using calibration data. Finally, thethird column represents the call made based on experimental data thatwas corrected for assay system noise using calibration data. As can beseen in the heading, the calls made using the uncorrected experimentaldata were correct 40% of the time, while the calls made using thecorrected experimental data were correct 90% of the time. Moreover, thesamples marked “ntc” are no-template controls, are negative controls forwhich no melt curve would be expected. The corrected MCD consistentlyassigns the negative controls correctly. Accordingly, variousembodiments of methods for the analysis of dissociation melt curve dataas depicted in FIG. 1 are effective in making determinations ofgenotyping, where the melting temperatures in an experimental set ofdata are different by only fractions of a degree.

According to various embodiments of methods for the analysis ofdissociation melt curve data, the experimental melt curve data can befurther analyzed to detect true differences in data that are differentby only fraction of a degree. According to various embodiments, in step150 of FIG. 2, difference data may be generated using the experimentaldata. A plot of difference data for a set of experimental data isdisplayed in FIG. 12, and the corresponding samples are shown in thetable of FIG. 11. In FIG. 12, the melt curve data for the wild typesample is taken as the data from which all other melt curve data for allother samples will be compared. The differences are taken between themelt curve data, and the wild type, and plotted in FIG. 12. The scale onthe abscissa is set as previously described. The ordinate scale is arelative scale based on the reference melt curve data defined as zero,by definition, and the minimum and maximum values set by greatestmagnitude offset in the difference data. The data of interestcorresponds to the attributes of the peaks in the positive region of thescale. The difference plots in FIG. 12 are labeled with respect to thecorresponding samples listed in the table of FIG. 11.

In the table of FIG. 11, the melting temperature, T_(m), is shown in thefirst column for the samples. In the second column, designated DeltaMax, the values entered in that column refer to the value on therelative scale of the difference between the wild type and a samplepeak, for the peaks in the positive region of the scale. In the thirdcolumn, T_(Deltamax) is the corresponding temperature at Delta Max. Inthe last column, the sum of the absolute difference (SAD) is the areaunder the sample peak. Therefore, various embodiments of step 150 ofFIG. 2 are demonstrated in FIG. 11 and FIG. 12, in which the creation ofdifference data, as shown in the plots of FIG. 12, becomes the basis ofgenerating feature vectors in addition to the melting temperatures, suchas Delta Max, T_(Deltamax), and SAD.

According to various embodiments of methods for the analysis ofdissociation melt curve data as indicated by step 160 of FIG. 2, thefeature vectors can be used to further discriminate differences in a setof data, where the melting temperatures are different by only a fractionof a degree. For example, in the table of FIG. 11, the samples are knownto be samples that should not be clustered. That is, unlike the datarepresented in FIG. 9, for which there were multiple samples, orclusters of samples, for a genotype, for the data represented in FIG.11, the samples should be distinct. Using the features vectors providesmore information for which samples having melting temperatures that aredifferent by only a fraction of a degree may be further discriminated.Through the inspection of the data in the table of FIG. 11, it isapparent that sample 2, having a T_(m) of 84.1° C. is different fromsamples 3 and 4, which have the same T_(m) of 84.2° C., by only 0.1° C.However, the Delta Max for the three samples is strikingly different,and clearly differentiates them. In this regard, the use of anadditional feature vector may be used to further discriminate thesamples.

Likewise, the block of data indicated with hatching; samples 5-10, allhave melting temperatures of 84.5° C. Though most of the samples may befurther discriminated by using Delta Max, samples 8 and 9 are onlydistinguished using the SAD feature vector. According to variousembodiments of methods for the analysis of dissociation melt curve datain step 160 of FIG. 2, some or any combination of the feature vectorsmay be used to further evaluate EMCD. In various embodiments of step 160of FIG. 2, the EMCD may be sorted by feature vectors sequentially, andan evaluation of fit may be made at after each iteration. According tovarious embodiments of step 160 of FIG. 2, the EMCD may be sorted by onefeature vector or any combination of feature vectors as an iterativeprocess, and an evaluation of the data may be evaluation of fit may bemade after each iteration

While the principles of this invention have been described in connectionwith specific embodiments of methods for analyzing dissociation meltcurve data, it should be understood clearly that these descriptions aremade only by way of example and are not intended to limit the scope ofthe invention. What has been disclosed herein has been provided for thepurposes of illustration and description. It is not intended to beexhaustive or to limit what is disclosed to the precise forms described.Many modifications and variations will be apparent to the practitionerskilled in the art. What is disclosed was chosen and described in orderto best explain the principles and practical application of thedisclosed embodiments of the art described, thereby enabling othersskilled in the art to understand the various embodiments and variousmodifications that are suited to the particular use contemplated. It isintended that the scope of what is disclosed be defined by the followingclaims and their equivalence.

What is claimed is:
 1. A method for analyzing melt curve data, themethod comprising: generating, by a computer system, melt curve data fora calibration sample deposited in a plurality of support regions of asample support device in a thermal cycler system, wherein the melt curvedata is a calibration set of melt curve data, operable for correctingother sets of melt curve data; generating, by the computer system, meltcurve data for at least one test sample deposited in a plurality ofsupport regions of a sample support device in thermal cycler system,wherein the melt curve data is an experimental set of melt curve data;correcting, by signal processing by the computer system, theexperimental set of melt curve data for system noise variation over theplurality of support regions of the sample support using the calibrationset of melt curve data; and displaying, by the computer system, thecorrected experimental set of melt curve data to a user.
 2. The methodof claim 1, wherein the correction is done on a derivative form of themelt curve data.
 3. The method of claim 1, wherein the at least one testsample is a plurality of test samples.
 4. The method of claim 3, furthercomprising the step of scaling the corrected experimental set of meltcurve data over an estimated temperature range.
 5. The method of claim4, further comprising the step of fitting the scaled experimental set ofmelt curve data to an estimated asymptote for a low temperature regionof a melting region of the melt curve data.
 6. The method of claim 5,further comprising the step of clustering the experimental set of meltcurve.
 7. The method of claim 5, further comprising the step of creatingdifference data from the corrected experimental set of melt curve data,wherein the melt curve data for a test sample in the plurality ofsamples is selected as a reference, and the melt curve data for theremaining samples are subtracted from the reference.
 8. The method ofclaim 7, further comprising the step of generating a set of featurevectors from the difference data, wherein a feature vector is anattribute used to cluster the experimental set of melt curve data intosubgroups.
 9. The method of claim 8, and further comprising the step ofclustering the experimental set of melt curve data into subgroups basedon at least one feature vector.
 10. The method of claim 1, wherein thecorrection of the experimental set of melt curve data using thecalibration set of melt curve data is a correction for assay systemnoise.
 11. The method of claim 10, wherein the source of assay systemnoise is thermal non-uniformity.
 12. The method of claim 10, wherein thesource of assay system noise is excitation source non-uniformity. 13.The method of claim 10, wherein the source of assay system noise isdetection noise.
 14. The method of claim 1, wherein curve fitting stepsare performed on the calibration set of melt curve data.
 15. The methodof claim 14, further comprising estimating an asymptote at a lowtemperature end based on the calibration set of melt curve data.
 16. Amethod for analyzing melt curve data, the method comprising: generating,by a computer system, melt curve data for a calibration sample depositedin a plurality of support regions of a sample support device in athermal cycler system, wherein the melt curve data is a calibration setof melt curve data; generating, by the computer system, melt curve datafor at least one test sample deposited in a plurality of support regionsof a sample support device in thermal cycler system, wherein the meltcurve data is an experimental set of melt curve data; correcting, bysignal processing by the computer system, the experimental set of meltcurve data using the calibration set of melt curve data, wherein thecorrection of the experimental set of melt curve data by the calibrationset of melt curve data removes system variance over the plurality ofsupport regions from the experimental set of melt curve data; analyzing,by the computer system, the corrected experimental set of melt curvedata, wherein the removal of system variance from the experimental setof melt curve data enhances analysis of small variations within theexperimental set of melt curve data; and displaying the correctedexperimental set of melt curve data.
 17. The method of claim 16, whereinthe sample support device with a calibration sample deposited in aplurality of support regions and the sample support device with at leastone test sample deposited in a plurality of support regions aredifferent sample support devices.
 18. The method of claim 16, whereinthe sample support device with a calibration sample deposited in aplurality of support regions and the sample support device with at leastone test sample deposited in a plurality of support regions are the samesupport device.
 19. A system for analyzing melt curve data, the systemcomprising: a detection system configured to: generate melt curve datafor a calibration sample deposited in a plurality of support regions ofa sample support device in a thermal cycler system, wherein the meltcurve data is a calibration set of melt curve data, operable forcorrecting other sets of melt curve data, generate melt curve data forat least one test sample deposited in a plurality of support regions ofa sample support device in thermal cycler system, wherein the melt curvedata is an experimental set of melt curve data; and a computer systemconfigured to: correct, by signal processing, the experimental set ofmelt curve data for system noise variation over the plurality of supportregions of the sample support using the calibration set of melt curvedata, and display the corrected experimental set of melt curve data to auser.
 20. The system of claim 19, wherein the correction is done on aderivative form of the melt curve data.
 21. The system of claim 19,wherein the at least one test sample is a plurality of test samples. 22.The system of claim 21, wherein the computer system is furtherconfigured to scale the corrected experimental set of melt curve dataover an estimated temperature range.